CapsNets are trained via standard back-propagation learning, minimizing some loss function like margin loss. Our ultimate goal is to assess to which extent a variation of the value of some parameter \(\theta \)  would affect the error on the network output. In particular, the parameters not affecting the network output can be pushed to zero in a soft manner, meaning that we can apply an \(\ell _2\)  penalty term. A number of approaches have been proposed, especially in the recent years [1]}, [2]}, [3]}. One recent state-of-the-art approach, LOBSTER [4]} proposes to penalize the parameters by their gradient-weighted \(\ell _2\)  norm, leading to the update rule
\(\theta ^{t+1} = &\theta ^t - \eta G\left[\frac{\partial L}{\partial \theta ^t}\right] - \lambda \theta ^t \textrm {ReLU}\left[1- \left| \frac{\partial L}{\partial \theta ^t}\right| \right],\) 
